From the Ground Up: Horizontal Accuracy Assessment in LiDAR.

Project
specifications typically spell out accuracy requirements for both the
horizontal and vertical components in LiDAR collection.
But while a thorough assessment and reporting of the vertical accuracy of the
elevation surface is almost always completed for LiDAR projects, a formal
evaluation of the horizontal accuracy is less often required.

Project
specifications typically spell out accuracy requirements for both the
horizontal and vertical components in LiDAR collection.

But while a thorough assessment and reporting of the vertical accuracy of the
elevation surface is almost always completed for LiDAR projects, a formal
evaluation of the horizontal accuracy is less often required.

It is common to see accuracy requirements in the form of a root mean square
error (RMSE) that is then used to approximate the accuracy of the elevation
surface. Maximum vertical RMSEs of 15 to 18.5 centimeters (0.49 to 0.61 feet)
are common for elevation models used to generate 2-foot contours. Similarly,
horizontal accuracies (in terms of RMSEs) ranging from 0.5 to 1.0 meter are
often specified for these same elevation models.

The collection of check points and the ensuing statistical assessment of the
vertical surface are relatively easy and straightforward. Evaluating the
horizontal accuracy is significantly more challenging, but with proper
planning, it can be successfully accomplished.

Figure
1. The control point visually fits the
southeast corner of the parking lot in this LiDAR intensity image. While
the image resolution is relatively coarse, many features are clearly visible,
including paint striping on the roadway that lies to the east of the parking
lot.

Challenges
in Horizontal
Accuracy Evaluation

Traditional vector-based mapping and digital orthophotos are often evaluated in
terms of their horizontal accuracy, and reporting is provided as a project
deliverable. What makes a LiDAR collection different? The answer lies in the
nature of LiDAR collection and the relative coarseness of the elevation points
collected.[1] Features that are crisp in imagery are much harder to precisely
define in coarse imagery generated from LiDAR returns.

All LiDAR projects are planned for some amount of side overlap between adjacent
flight lines. This is similar to the requirement for side overlap in the
collection of aerial imagery. While most specifications require 30-percent
overlap for imagery collection (indicating that 30 percent of the ground
coverage in one flight line is also found in the adjacent flight line), it is
fairly common to have side overlap ranging from 20 to 40 percent or more with
LiDAR collection. The amount of planned overlap depends on a number of
variables, including required accuracies, the presence of development and the
amount of terrain relief found at the project site.

Comparisons made in the side overlap area provide the first glimpse of the
horizontal accuracy of LiDAR data. This analysis is particularly valuable when
comparing adjacent flight lines that were flown in opposing directions.
Buildings with pitched roofs or a sloped ground surface should be evaluated in
the area of overlap. These features should fit almost perfectly on top of one
another if the system is properly calibrated, the acquisition is carried out
under strong GPS configurations, and the post processing is completed in an
accurate fashion.

Some potential horizontal errors, however, will not be evident in a comparison
of opposing flight lines in the side overlap area. It is necessary to take the
analysis one step further to completely evaluate the horizontal accuracies
gained in the LiDAR collection.

Figure
2. The individual intensities are draped
over the digital orthophoto in this portion of the accuracy assessment. The
high-intensity returns from the roadway striping fit in the orthophoto.

Accuracy
Analysis

At least three strategies can be used to provide a reasonable test of
horizontal accuracies from LiDAR. Intensity images are used in two of the three
most common assessment methods. During LiDAR collection, units determine the 3D
position of the laser as it is reflected off the ground surface. The sensors
also measure the amount of energy in the reflected signal. This is known as the
intensity of the return. Metal roofs have very different intensities compared
to asphalt shingles. Concrete returns significantly more energy than asphalt.
Deciduous trees have varying intensities when compared to closely mown grass.
These intensities are perfectly registered to the 3D position of each laser
pulse. We often generate images from these intensities to aid in the overall
post processing or to be used in the collection of additional information, such
as breaklines, to enhance the LiDAR surface.

For the first method of accuracy analysis, features must be carefully selected
within the project area at points that will be visible in an intensity image so
that their horizontal position can be determined with field GPS procedures and
then compared to their location in the intensity image. This situation is
analogous to the accuracy assessment of traditional mapping, but the difficulty
of identifying the precise location of a feature in a coarse intensity image
provides a higher degree of uncertainty when compared to traditional
imagery.

The intensity image in Figure 1 on page 54 provides a good example. The red “x”
marks the horizontal location of a photo-identifiable ground control point that
was collected at the southeast corner of a parking lot. Notice the scan lines
from the LiDAR following along a northeast-southwest pattern. During our accuracy
assessment for this project, lines were constructed along the southern and
eastern limits of the parking lot from the visual interpretation of the
intensities, and we then refined these lines by looking at the elevation
surface. The elevation surface was useful because the control point was
collected at the base of the curb. The intersection of these two lines was then
compared to the position gained from GPS. In this case, the LiDAR
interpretation fit within 1.7 feet of the GPS position.

We have confidence that this comparison was accurate within 1.0 foot given the
use of both intensities and the elevation surface to determine the corner of
the parking lot. Other features also work well in the assessment of horizontal
accuracy. Painted features like stop bars or striping in parking lots provide
very good opportunities for control points. The paint shows up well in
intensity images because it is a highly reflective surface and returns most of
the energy from the laser. Transitions from concrete to asphalt surfaces, which
are common where sidewalks meet driveways or roads, also serve as good control
points.

With the second assessment method, it is possible to overlay the intensity
image on digital orthophotos if they exist for the project area. However, to be
valid in an accuracy assessment, the digital orthophotos must be of
sufficiently higher accuracy than the project requirements for horizontal
accuracy. The paint striping along roadways and in parking lots again provides
excellent opportunities for the validation of the horizontal accuracy. Water
features (where most of the laser returns are absorbed) and asphalt surfaces
can also provide valid checks.
Figure 2 illustrates how well the LiDAR intensities for this project fit the orthophotos.
The higher intensities are rendered as bright white. Notice how well these
higher intensities fit with the paint striping on the roadway and the stop bar
painted at the intersection. You can also see how well the low-intensity
returns (rendered as darker shades of gray) fit over surfaces that tend to
reflect a small amount of energy like, in this case, asphalt roadways and
driveways.

To be valid, roadways running in multiple directions should be carefully
reviewed. A systematic east-west shift of the LiDAR data would not show up in a
straight section of roadway running east-west. But confidence is gained if
there is a close match in both east-west and north-south roads within a project
area.

A third way to validate horizontal accuracy is to compare field run cross
sections against sections generated from the LiDAR elevation model. The
sections should be collected in areas with significant slope. Levees, roadway
embankments or streams can provide ideal surfaces for this comparison. The
argument for different orientations for the comparisons made above would apply
here, as well. Ideally the sections will coincide with one another. Any
horizontal shifts in the data will be evident in the cross sections as an
offset between the two data sets and can easily be measured and compared
against project requirements.

As technology changes, the challenges of a horizontal assessment are somewhat
mitigated. A prime example is the hardware improvements that provide us with
the ability to collect more dense elevation data sets. Increased point density
makes it much easier to define photo-identifiable features in an intensity
image and compare those features to field-determined GPS positions.

The horizontal integrity of a LiDAR surface is critically important to all
projects, but a formal assessment is less frequently required as a project
deliverable than the assessment of the vertical integrity. While major
horizontal blunders would almost certainly show up in the vertical assessment,
smaller horizontal errors could be missed if only elevation checks on
relatively flat surfaces are performed. But with proper planning, the more
challenging horizontal accuracy assessment can also be successfully accomplished
and included as a valuable project deliverable.

Reference

1- In my July 2008 column, “From
the Ground Up: Collecting Breaklines,” I compared the resolution of a typical
LiDAR collection for a 2-foot elevation surface to that of digital imagery. In
this example, the LiDAR resolution is 1 to 3 points per square meter compared
to 43 points in 1/2-foot imagery and 172 points per square meter in 1/4-foot
imagery.

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